When reading a conventional or an electronic book, a user often encounters interesting or strange terms that he or she wants to have more knowledge about, in addition to what the book itself presents. Mostly likely, the knowledge is readily available on the Internet. For example, online encyclopedia databases, such as Wikipedia, are popular resources that contain a very large amount of information covering almost every conceivable subject matter. Conventionally, the user can find a computing device connected to the Internet, open an internet browser to visit Wikipedia, and then submit his or her search term to get the relevant information on the book term. The user may find the process cumbersome and interruptive and so may give up the intention for a deep dive experience.
“Wikification” refers to the task of automatically linking text-based content to Wikipedia entries corresponding to terms mentioned in the text. Common terms of interest include people, places, organizations and similar categories. Typically a Wikification process involves implementation of two primary steps: (1) detection of suitable candidate terms that are potentially interesting to a user, and (2) disambiguation of some candidate terms that may match to several Wikipedia entries, or webpages.
However, an entry in the Wikipedia or similar information source sites usually includes some segment of information with low relevancy which an average user can hardly find useful even for a deep dive experience. Also, because relevant information may be acquired in more than one entry from either a single or from multiple information sources, presenting these scattered relevant information in its raw form, such as in different pages, inconsistent text formats, and varying categories of content, can make a deep dive experience inefficient and unpleasant.